Antoine C.

Built an end-to-end Python data pipeline to analyze the future skills demand in the tech sector.

The project involved scraping 6,000+ technology job postings, cleaning and structuring unstructured data, and extracting key technical and transversal skills using Python.

Skills were then mapped to European competency frameworks (DigComp & GreenComp) to support competency redesign and curriculum alignment.

Results were delivered through interactive dashboards and a structured analytical report, enabling clear insights for decision-makers.

Tools & methods

Python (data scraping, cleaning, NLP-based skill extraction)

Data analysis & structuring

Competency framework mapping

Data visualization & reporting

Outcome

Actionable insights on emerging tech skills and sustainability competencies, supporting strategic decisions in education and workforce planning.

Conducted a research-driven evaluation of data reduction algorithms to assess their viability, performance, and scalability in comparison with the industry-standard VDO solution.

The work involved designing benchmarking protocols, analyzing performance metrics, and testing scalability under realistic data volumes and workloads. Results were synthesized into clear analytical insights to support technical decision-making and algorithm selection.

Tools & methods

Algorithm benchmarking and evaluation

Performance and scalability analysis

Large-scale data handling

Comparative analysis with industry standards (VDO)

Outcome

Provided evidence-based recommendations on the feasibility of alternative data reduction approaches for large-scale data storage systems.